Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Multi-dimensional sequential pattern mining
Proceedings of the tenth international conference on Information and knowledge management
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IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Analyzing the Subjective Interestingness of Association Rules
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Mining Mutually Dependent Patterns
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Peculiarity Oriented Multidatabase Mining
IEEE Transactions on Knowledge and Data Engineering
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
Mining Surprising Periodic Patterns
Data Mining and Knowledge Discovery
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns from Multidimensional Sequence Data
IEEE Transactions on Knowledge and Data Engineering
Unified algorithm for undirected discovery of exception rules: Research Articles
International Journal of Intelligent Systems - Knowledge Discovery: Dedicated to Jan M. Żytkow
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
M2SP: mining sequential patterns among several dimensions
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Outlier detection in relational data: A case study in geographical information systems
Expert Systems with Applications: An International Journal
S2MP: similarity measure for sequential patterns
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
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Discovering unexpected rules is essential, particularly for industrial applications with marketing stakes. In this context, many works have been done for association rules. However, none of them addresses sequences. In this paper, we thus propose to discover unexpected multidimensional sequential rules in data cubes. We define the concept of multidimensional sequential rule, and then unexpectedness. We formalize these concepts and define an algorithm for mining this kind of rules. Experiments on a real data cube are reported and highlight the interest of our approach.